U.S. patent application number 16/278331 was filed with the patent office on 2019-06-13 for data backup management during workload migration.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to VALENTINA SALAPURA, MAJA VUKOVIC.
Application Number | 20190179709 16/278331 |
Document ID | / |
Family ID | 58282817 |
Filed Date | 2019-06-13 |
United States Patent
Application |
20190179709 |
Kind Code |
A1 |
SALAPURA; VALENTINA ; et
al. |
June 13, 2019 |
DATA BACKUP MANAGEMENT DURING WORKLOAD MIGRATION
Abstract
Managing data backup during workload migration is provided. A
set of workloads for migration from a source environment to a
target environment is identified in response to receiving a request
to migrate the set of workloads. The migration of the set of
workloads is initiated from the source environment to the target
environment along with migration of backup data corresponding to
the set of workloads. A backup configuration transformation from a
backup configuration corresponding to the source environment to a
set of backup configurations corresponding to the target
environment is determined based on semantic matching between
characteristics of the backup configuration corresponding to the
source environment and characteristics of the set of backup
configurations corresponding to the target environment, a state of
the source environment, backup configuration transformation
actions, and a goal state of the target environment.
Inventors: |
SALAPURA; VALENTINA;
(CHAPPAQUA, NY) ; VUKOVIC; MAJA; (NEW YORK,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
58282817 |
Appl. No.: |
16/278331 |
Filed: |
February 18, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14859968 |
Sep 21, 2015 |
10255136 |
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16278331 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/10 20130101;
G06F 2009/4557 20130101; G06F 11/1448 20130101; G06F 11/1458
20130101; G06F 16/214 20190101 |
International
Class: |
G06F 11/14 20060101
G06F011/14; G06F 16/21 20060101 G06F016/21 |
Claims
1. A computer-implemented method for managing data backup of
workloads, the workloads being migrated from a source environment
to a target environment, the computer-implemented method
comprising: identifying, by a computer, a set of workloads for
migration from the source environment to the target environment in
response to receiving a request to migrate the set of workloads;
initiating, by the computer, the migration of the set of workloads
from the source environment to the target environment along with
migration of the backup data corresponding to the set of workloads;
and determining, by the computer, a backup configuration
transformation from a backup configuration corresponding to the
source environment to a set of backup configurations corresponding
to the target environment based on semantic matching between
characteristics of the backup configuration corresponding to the
source environment and characteristics of the set of backup
configurations corresponding to the target environment, a state of
the source environment, backup configuration transformation
actions, and a goal state of the target environment, wherein the
characteristics include data dependencies between virtual machines
executing the set of workloads and wherein the set of workloads is
migrated in waves based on the data dependencies.
2. The computer-implemented method of claim 1 further comprising:
analyzing, by the computer, the characteristics of the backup
configuration corresponding to the source environment for each
workload in the set of workloads for the migration to the target
environment; and analyzing, by the computer, the characteristics of
the set of backup configurations corresponding to the target
environment, wherein the backup data migration represents a
migration of all backed up data corresponding to the set of
workloads being migrated in the set of workloads migration.
3. The computer-implemented method of claim 2 further comprising:
performing, by the computer, the semantic matching between the
characteristics of the backup configuration corresponding to the
source environment and the characteristics of the set of backup
configurations corresponding to the target environment for each
backup capability.
4. The computer-implemented method of claim 1 further comprising:
defining, by the computer, the backup configuration transformation
actions by representing each workload migration step in a set of
workload migration steps in terms of input, output, precondition,
and post-condition effect.
5. The computer-implemented method of claim 1 further comprising:
generating, by the computer, a backup configuration transformation
plan based on the backup configuration transformation from the
backup configuration corresponding to the source environment to the
set of backup configurations corresponding to the target
environment, wherein the backup data migration represents a
migration of all backed up data corresponding to the set of
workloads being migrated in the set of workloads migration.
6. The computer-implemented method of claim 5 further comprising:
executing, by the computer, the backup configuration transformation
plan using a set of application programming interfaces.
7. The computer-implemented method of claim 1 further comprising:
monitoring, by the computer, the backup configuration
transformation for an exception, wherein the exception is one of an
unknown backup configuration exception, a new data backup
technology exception, a change in target environment exception, or
an unknown exception.
8. The computer-implemented method of claim 7 further comprising:
sending, by the computer, a notification to a subject matter expert
to review the unknown exception; receiving, by the computer, a set
of modifications to a backup configuration transformation plan
based on the review of the subject matter expert of the unknown
exception; and modifying, by the computer, the backup configuration
transformation plan based on the set of modifications.
9. The computer-implemented method of claim 1, wherein the source
environment is a data center environment, and wherein the target
environment is a cloud environment.
10. The computer-implemented method of claim 1, wherein the source
environment is a data center environment, and wherein the target
environment is a hybrid cloud environment that includes a set of
different cloud environments.
11. The computer-implemented method of claim 1, wherein the
computer migrates the set of workloads and the backup data
corresponding to the set of workloads from the source environment
to the target environment concurrently.
12. A computer system for managing data backup, the computer system
comprising: a bus system; a storage device connected to the bus
system, wherein the storage device stores program instructions; and
a processor connected to the bus system, wherein the processor
executes the program instructions to: identify a set of workloads
for migration from a source environment to a target environment in
response to receiving a request to migrate the set of workloads;
initiate the migration of the set of workloads from the source
environment to the target environment along with migration of the
backup data corresponding to the set of workloads; and determine a
backup configuration transformation from a backup configuration
corresponding to the source environment to a set of backup
configurations corresponding to the target environment based on
semantic matching between characteristics of the backup
configuration corresponding to the source environment and
characteristics of the set of backup configurations corresponding
to the target environment, a state of the source environment,
backup configuration transformation actions, and a goal state of
the target environment, wherein the characteristics include data
dependencies between virtual machines executing the set of
workloads and wherein the set of workloads is migrated in waves
based on the data dependencies.
13. The computer system of claim 12, wherein the processor further
executes the program instructions to: analyze the characteristics
of the backup configuration corresponding to the source environment
for each workload in the set of workloads for the migration to the
target environment; and analyze the characteristics of the set of
backup configurations corresponding to the target environment,
wherein the backup data migration represents a migration of all
backed up data corresponding to the set of workloads being migrated
in the set of workloads migration.
14. A computer program product for managing data backup, the
computer program product comprising a computer readable storage
medium having program instructions embodied therewith, the program
instructions executable by a computer to cause the computer to
perform a method comprising: identifying, by the computer, a set of
workloads for migration from a source environment to a target
environment in response to receiving a request to migrate the set
of workloads; initiating, by the computer, the migration of the set
of workloads from the source environment to the target environment
along with migration of backup data corresponding to the set of
workloads; and determining, by the computer, a backup configuration
transformation from a backup configuration corresponding to the
source environment to a set of backup configurations corresponding
to the target environment based on semantic matching between
characteristics of the backup configuration corresponding to the
source environment and characteristics of the set of backup
configurations corresponding to the target environment, a state of
the source environment, backup configuration transformation
actions, and a goal state of the target environment, wherein the
characteristics include data dependencies between virtual machines
executing the set of workloads and wherein the set of workloads is
migrated in waves based on the data dependencies.
15. The computer program product of claim 14 further comprising:
analyzing, by the computer, the characteristics of the backup
configuration corresponding to the source environment for each
workload in the set of workloads for the migration to the target
environment; and analyzing, by the computer, the characteristics of
the set of backup configurations corresponding to the target
environment, wherein the backup data migration represents a
migration of all backed up data corresponding to the set of
workloads being migrated in the set of workloads migration.
16. The computer program product of claim 15 further comprising:
performing, by the computer, the semantic matching between the
characteristics of the backup configuration corresponding to the
source environment and the characteristics of the set of backup
configurations corresponding to the target environment for each
backup capability.
17. The computer program product of claim 14 further comprising:
defining, by the computer, the backup configuration transformation
actions by representing each workload migration step in a set of
workload migration steps in terms of input, output, precondition,
and post-condition effect.
18. The computer program product of claim 14 further comprising:
generating, by the computer, a backup configuration transformation
plan based on the backup configuration transformation from the
backup configuration corresponding to the source environment to the
set of backup configurations corresponding to the target
environment, wherein the backup data migration represents a
migration of all backed up data corresponding to the set of
workloads being migrated in the set of workloads migration.
19. The computer program product of claim 18 further comprising:
executing, by the computer, the backup configuration transformation
plan using a set of application programming interfaces.
20. The method of claim 1, further comprising: forming the backup
data corresponding to the set of workloads by performing a data
backup for all virtual images in a particular wave before migrating
the set of workloads from the source environment to the target
environment.
21. The computer system of claim 12, further comprising: forming
the backup data corresponding to the set of workloads by performing
a data backup for all virtual images in a particular wave before
migrating the set of workloads from the source environment to the
target environment.
22. The computer program product of claim 14, further comprising:
forming the backup data corresponding to the set of workloads by
performing a data backup for all virtual images in a particular
wave before migrating the set of workloads from the source
environment to the target environment.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/859,968, filed Sep. 21, 2015, entitled "
Data Backup Management During Workload Migration," status Allowed,
attorney docket number YOR920150740US1, which is incorporated
herein by reference in its entirety.
BACKGROUND
1. Field
[0002] The disclosure relates generally to data backup management
and more specifically to managing data backup configuration
transformation from a backup configuration corresponding to a
source virtual machine environment to a set of backup
configurations corresponding to a target virtual machine
environment during migration of a set of workloads from the source
virtual machine environment to the target virtual machine
environment.
2. Description of the Related Art
[0003] Several companies sell online data backup services for
saving data files to a cloud environment. These online data backup
services can restore saved data files to a host computer, for
example. In addition, these online data backup services may allow a
user to retrieve the stored data files with a smart phone or tablet
computer or email the stored files to a friend or colleague. While
saving data files to a cloud may be convenient and a way to
automate data backups, the initial data backup may be slow, taking
up to several days, depending on the amount of data to be backed up
and the speed of the network connection. In addition, the online
data backup services may only back up user-created data files, such
as personal files, and not system files, such as those system files
required to boot up a system. Thus, these online data backup
services only provide partial data backup protection. Further,
these online data backup services may only enable backup of a
single device, which is not suitable for backup of a data center,
for example.
SUMMARY
[0004] According to one illustrative embodiment, a
computer-implemented method for managing data backup during
workload migration is provided. A computer identifies a set of
workloads for migration from a source environment to a target
environment in response to receiving a request to migrate the set
of workloads. The computer initiates the migration of the set of
workloads from the source environment to the target environment
along with migration of backup data corresponding to the set of
workloads. The computer determines a backup configuration
transformation from a backup configuration corresponding to the
source environment to a set of backup configurations corresponding
to the target environment based on semantic matching between
characteristics of the backup configuration corresponding to the
source environment and characteristics of the set of backup
configurations corresponding to the target environment, a state of
the source environment, backup configuration transformation
actions, and a goal state of the target environment. According to
other illustrative embodiments, a computer system and computer
program product for managing data backup during workload migration
are provided.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a pictorial representation of a network of data
processing systems in which illustrative embodiments may be
implemented;
[0006] FIG. 2 is a diagram of a data processing system in which
illustrative embodiments may be implemented;
[0007] FIG. 3 is a diagram illustrating a cloud computing
environment in which illustrative embodiments may be
implemented;
[0008] FIG. 4 is a diagram illustrating an example of abstraction
layers of a cloud computing environment in accordance with an
illustrative embodiment;
[0009] FIG. 5 is a diagram of an example of a migration process in
accordance with an illustrative embodiment;
[0010] FIG. 6 is a diagram of an example of an alternate migration
process in accordance with an illustrative embodiment;
[0011] FIG. 7 is a specific example of backup configuration
transformation inputs in accordance with an illustrative
embodiment; and
[0012] FIGS. 8A-8C are a flowchart illustrating a process for
managing data backup during workload migration in accordance with
an illustrative embodiment.
DETAILED DESCRIPTION
[0013] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0014] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0015] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0016] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0017] Aspects of the present invention are described below with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0018] These computer program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0019] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0020] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0021] With reference now to the figures, and in particular, with
reference to FIGS. 1-3, diagrams of data processing environments
are provided in which illustrative embodiments may be implemented.
It should be appreciated that FIGS. 1-3 are only meant as examples
and are not intended to assert or imply any limitation with regard
to the environments in which different embodiments may be
implemented. Many modifications to the depicted environments may be
made.
[0022] FIG. 1 depicts a pictorial representation of a network of
data processing systems in which illustrative embodiments may be
implemented. Network data processing system 100 is a network of
computers and other devices in which the illustrative embodiments
may be implemented. Network data processing system 100 contains
network 102, which is the medium used to provide communications
links between the computers and the other devices connected
together within network data processing system 100. Network 102 may
include connections, such as, for example, wire communication
links, wireless communication links, and fiber optic cables.
[0023] In the depicted example, server 104 and server 106 connect
to network 102, along with storage 108. Server 104 and server 106
may be, for example, server computers with high-speed connections
to network 102. In addition, server 104 and server 106 may provide
services, such as, for example, managing client workload migration
from a source virtual machine environment, such as a data center
environment, to a target virtual machine environment, such as a
cloud environment, and managing data backup configuration
transformation from a data backup configuration corresponding to
the source virtual machine environment to a set of data backup
configurations corresponding to the target virtual machine
environment during migration of the client workload. The backup
data corresponds to the client workload being migrated.
[0024] Client 110, client 112, and client 114 also connect to
network 102. Clients 110, 112, and 114 are clients of server 104
and server 106. Server 104 and server 106 may provide information,
such as boot files, operating system images, virtual machine
images, and software applications to clients 110, 112, and 114.
[0025] In this example, clients 110, 112, and 114 may each
represent a different virtual machine environment. A virtual
machine environment includes physical resources used to host and
execute virtual machines to perform a set of one or more workloads
or tasks. A virtual machine environment may comprise, for example,
one server, a rack of servers, a cluster of servers, such as a data
center, a cloud of computers, such as a private cloud, a public
cloud, or a hybrid cloud, or any combination thereof. However, it
should be noted that clients 110, 112, and 114 are intended as
examples only. In other words, clients 110, 112, and 114 may
include other types of data processing systems, such as, for
example, network computers, desktop computers, laptop computers,
tablet computers, handheld computers, smart phones, personal
digital assistants, and gaming devices.
[0026] Storage 108 is a network storage device capable of storing
any type of data in a structured format or an unstructured format.
The type of data stored in storage 108 may be, for example, lists
of source virtual machine environments, lists of target virtual
machine environments, characteristics or properties of each listed
source and target virtual machine environment, and backup
configuration transformation plans for transforming a source
environment's data backup configuration to a target environment's
data backup configuration during migration of a workload from the
source environment to the target environment. Further, storage unit
108 may store other types of data, such as authentication or
credential data that may include user names, passwords, and
biometric data associated with system administrators.
[0027] In addition, it should be noted that network data processing
system 100 may include any number of additional servers, clients,
storage devices, and other devices not shown. Program code located
in network data processing system 100 may be stored on a computer
readable storage medium and downloaded to a computer or other data
processing device for use. For example, program code may be stored
on a computer readable storage medium on server 104 and downloaded
to client 110 over network 102 for use on client 110.
[0028] In the depicted example, network data processing system 100
may be implemented as a number of different types of communication
networks, such as, for example, an internet, an intranet, a local
area network (LAN), and a wide area network (WAN). FIG. 1 is
intended as an example only, and not as an architectural limitation
for the different illustrative embodiments.
[0029] With reference now to FIG. 2, a diagram of a data processing
system is depicted in accordance with an illustrative embodiment.
Data processing system 200 is an example of a computer, such as
server 104 in FIG. 1, in which computer readable program code or
instructions implementing processes of illustrative embodiments may
be located. In this illustrative example, data processing system
200 includes communications fabric 202, which provides
communications between processor unit 204, memory 206, persistent
storage 208, communications unit 210, input/output (I/O) unit 212,
and display 214.
[0030] Processor unit 204 serves to execute instructions for
software applications and programs that may be loaded into memory
206. Processor unit 204 may be a set of one or more hardware
processor devices or may be a multi-processor core, depending on
the particular implementation. Further, processor unit 204 may be
implemented using one or more heterogeneous processor systems, in
which a main processor is present with secondary processors on a
single chip. As another illustrative example, processor unit 204
may be a symmetric multi-processor system containing multiple
processors of the same type.
[0031] Memory 206 and persistent storage 208 are examples of
storage devices 216. A computer readable storage device is any
piece of hardware that is capable of storing information, such as,
for example, without limitation, data, computer readable program
code in functional form, and/or other suitable information either
on a transient basis and/or a persistent basis. Further, a computer
readable storage device excludes a propagation medium. Memory 206,
in these examples, may be, for example, a random access memory, or
any other suitable volatile or non-volatile storage device.
Persistent storage 208 may take various forms, depending on the
particular implementation. For example, persistent storage 208 may
contain one or more devices. For example, persistent storage 208
may be a hard drive, a flash memory, a rewritable optical disk, a
rewritable magnetic tape, or some combination of the above. The
media used by persistent storage 208 may be removable. For example,
a removable hard drive may be used for persistent storage 208.
[0032] In this example, persistent storage 208 stores backup and
migration manager 218, workloads 220, backup data 222, backup
configurations 224, backup configuration transformation inputs 226,
backup configuration transformation plan 228, and backup
configuration transformation exception 230. However, illustrative
embodiments are not limited to such. In other words, persistent
storage 208 may store more or less information than
illustrated.
[0033] Backup and migration manager 218 controls the migration of a
set of one or more client workloads, such as workloads 220, from a
source virtual machine environment to a target virtual machine
environment, along with the migration of backup data, such as
backup data 222, corresponding to the set of one or more client
workloads to be migrated from the source to target environments.
The source virtual machine environment may be, for example, client
110 in FIG. 1. The target virtual machine environment may be, for
example, client 112 in FIG. 1. Further, backup and migration
manager 218 controls the backup configuration transformation from a
data backup configuration corresponding to the source environment
to a data backup configuration corresponding to the target
environment. It should be noted that even though backup and
migration manager 218 is illustrated as residing in persistent
storage 208, in an alternative illustrative embodiment backup and
migration manager 218 may be a separate component of data
processing system 200. For example, backup and migration manager
218 may be a hardware component coupled to communication fabric 202
or a combination of hardware and software components.
[0034] Workloads 220 represent a list of different workloads that
backup and migration manager 218 is to migrate from the source
environment to the target environment. Backup data 222 represent
the backed up data of the source environment corresponding to
workloads 220 that backup and migration manager 218 is to migrate
with workloads 220 from the source environment to the target
environment. Backup configurations 224 represent the data backup
configurations corresponding to the source and target environments,
such as source environment 232 and target environment 234. Source
environment 232 may represent, for example, a data center
environment. Target environment 234 may represent, for example, a
cloud environment. Characteristics 236 are the properties or
attributes of backup configurations 224. Characteristics 236 may
include, for example, data dependencies between virtual machines
executing workloads 220.
[0035] Backup and migration manager 218 utilizes backup
configuration transformation inputs 226 to transform the data
backup configuration corresponding to source environment 232 to the
data backup configuration corresponding to target environment 234.
In this example, backup configuration transformation inputs 226
include semantics of characteristics of backup configurations 240,
state of source environment 242, goal state of target environment
244, and backup configuration transformation contextual actions
246. Semantics of characteristics of backup configurations 240 are
descriptions of characteristics 236 for backup configurations 224.
State of source environment 242 is a current state of source
environment 232 prior to migration of workloads 220. Goal state of
target environment 244 is a goal state of target environment 234
after migration of workloads 220 and corresponding backup data 222.
Backup configuration transformation contextual actions 246 are a
set of one or more action steps that backup and migration manager
218 takes to achieve the backup configuration transformation from
the data backup configuration corresponding to source environment
232 to the data backup configuration corresponding to target
environment 234.
[0036] Backup configuration transformation plan 228 is a strategy
for transforming the data backup configuration corresponding to
source environment 232 to the data backup configuration
corresponding to target environment 234. Backup and migration
manager 218 generates backup configuration transformation plan 228
based on information in workloads 220, backup data 222, backup
configurations 224, and backup configuration transformation inputs
226. Backup configuration transformation exception 230 is a
possible exception that may be thrown when backup and migration
manager 218 executes backup configuration transformation plan 228.
Backup configuration transformation exception 230 may be, for
example, an unknown backup configuration exception, a new data
backup technology exception, a change in target environment
exception, or an unknown exception.
[0037] Communications unit 210, in this example, provides for
communication with other computers, data processing systems, and
devices via a network, such as network 102 in FIG. 1.
Communications unit 210 may provide communications through the use
of both physical and wireless communications links. The physical
communications link may utilize, for example, a wire, cable,
universal serial bus, or any other physical technology to establish
a physical communications link for data processing system 200. The
wireless communications link may utilize, for example, shortwave,
high frequency, ultra high frequency, microwave, wireless fidelity
(Wi-Fi), bluetooth technology, global system for mobile
communications (GSM), code division multiple access (CDMA),
second-generation (2G), third-generation (3G), fourth-generation
(4G), 4G Long Term Evolution (LTE), LTE Advanced, or any other
wireless communication technology or standard to establish a
wireless communications link for data processing system 200.
[0038] Input/output unit 212 allows for the input and output of
data with other devices that may be connected to data processing
system 200. For example, input/output unit 212 may provide a
connection for user input through a keypad, a keyboard, a mouse,
and/or some other suitable input device. Display 214 provides a
mechanism to display information to a user and may include touch
screen capabilities to allow the user to make on-screen selections
through user interfaces or input data, for example.
[0039] Instructions for the operating system, applications, and/or
programs may be located in storage devices 216, which are in
communication with processor unit 204 through communications fabric
202. In this illustrative example, the instructions are in a
functional form on persistent storage 208. These instructions may
be loaded into memory 206 for running by processor unit 204. The
processes of the different embodiments may be performed by
processor unit 204 using computer-implemented instructions, which
may be located in a memory, such as memory 206. These program
instructions are referred to as program code, computer usable
program code, or computer readable program code that may be read
and run by a processor in processor unit 204. The program
instructions, in the different embodiments, may be embodied on
different physical computer readable storage devices, such as
memory 206 or persistent storage 208.
[0040] Program code 248 is located in a functional form on computer
readable media 250 that is selectively removable and may be loaded
onto or transferred to data processing system 200 for running by
processor unit 204. Program code 248 and computer readable media
250 form computer program product 252. In one example, computer
readable media 250 may be computer readable storage media 254 or
computer readable signal media 256. Computer readable storage media
254 may include, for example, an optical or magnetic disc that is
inserted or placed into a drive or other device that is part of
persistent storage 208 for transfer onto a storage device, such as
a hard drive, that is part of persistent storage 208. Computer
readable storage media 254 also may take the form of a persistent
storage, such as a hard drive, a thumb drive, or a flash memory
that is connected to data processing system 200. In some instances,
computer readable storage media 254 may not be removable from data
processing system 200.
[0041] Alternatively, program code 248 may be transferred to data
processing system 200 using computer readable signal media 256.
Computer readable signal media 256 may be, for example, a
propagated data signal containing program code 248. For example,
computer readable signal media 256 may be an electro-magnetic
signal, an optical signal, and/or any other suitable type of
signal. These signals may be transmitted over communication links,
such as wireless communication links, an optical fiber cable, a
coaxial cable, a wire, and/or any other suitable type of
communications link. In other words, the communications link and/or
the connection may be physical or wireless in the illustrative
examples. The computer readable media also may take the form of
non-tangible media, such as communication links or wireless
transmissions containing the program code.
[0042] In some illustrative embodiments, program code 248 may be
downloaded over a network to persistent storage 208 from another
device or data processing system through computer readable signal
media 256 for use within data processing system 200. For instance,
program code stored in a computer readable storage media in a data
processing system may be downloaded over a network from the data
processing system to data processing system 200. The data
processing system providing program code 248 may be a server
computer, a client computer, or some other device capable of
storing and transmitting program code 248.
[0043] The different components illustrated for data processing
system 200 are not meant to provide architectural limitations to
the manner in which different embodiments may be implemented. The
different illustrative embodiments may be implemented in a data
processing system including components in addition to, or in place
of, those illustrated for data processing system 200. Other
components shown in FIG. 2 can be varied from the illustrative
examples shown. The different embodiments may be implemented using
any hardware device or system capable of executing program code. As
one example, data processing system 200 may include organic
components integrated with inorganic components and/or may be
comprised entirely of organic components excluding a human being.
For example, a storage device may be comprised of an organic
semiconductor.
[0044] As another example, a computer readable storage device in
data processing system 200 is any hardware apparatus that may store
data. Memory 206, persistent storage 208, and computer readable
storage media 254 are examples of physical storage devices in a
tangible form.
[0045] In another example, a bus system may be used to implement
communications fabric 202 and may be comprised of one or more
buses, such as a system bus or an input/output bus. Of course, the
bus system may be implemented using any suitable type of
architecture that provides for a transfer of data between different
components or devices attached to the bus system. Additionally, a
communications unit may include one or more devices used to
transmit and receive data, such as a modem or a network adapter.
Further, a memory may be, for example, memory 206 or a cache such
as found in an interface and memory controller hub that may be
present in communications fabric 202.
[0046] It should be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, illustrative embodiments are capable
of being implemented in conjunction with any other type of
computing environment now known or later developed. Cloud computing
is a model of service delivery for enabling convenient, on-demand
network access to a shared pool of configurable computing
resources, such as, for example, networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services, which can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0047] The characteristics may include, for example, on-demand
self-service, broad network access, resource pooling, rapid
elasticity, and measured service. On-demand self-service allows a
cloud consumer to unilaterally provision computing capabilities,
such as server time and network storage, as needed automatically
without requiring human interaction with the provider of the
service. Broad network access provides for capabilities that are
available over a network and accessed through standard mechanisms,
which promotes use by heterogeneous thin or thick client platforms,
such as, for example, mobile phones, laptops, and personal digital
assistants. Resource pooling allows the provider's computing
resources to be pooled to serve multiple consumers using a
multi-tenant model, with different physical and virtual resources
dynamically assigned and reassigned according to demand. There is a
sense of location independence in that the consumer generally has
no control or knowledge over the exact location of the provided
resources, but may be able to specify location at a higher level of
abstraction, such as, for example, country, state, or data center.
Rapid elasticity provides for capabilities that can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any time.
Measured service allows cloud systems to automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service, such as,
for example, storage, processing, bandwidth, and active user
accounts. Resource usage can be monitored, controlled, and reported
providing transparency for both the provider and consumer of the
utilized service.
[0048] Service models may include, for example, Software as a
Service (SaaS), Platform as a Service (PaaS), and Infrastructure as
a Service (IaaS). Software as a Service is the capability provided
to the consumer to use the provider's applications running on a
cloud infrastructure. The applications are accessible from various
client devices through a thin client interface, such as a web
browser (e.g., web-based e-mail). The consumer does not manage or
control the underlying cloud infrastructure including network,
servers, operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings. Platform as a Service is the
capability provided to the consumer to deploy onto the cloud
infrastructure consumer-created or acquired applications created
using programming languages and tools supported by the provider.
The consumer does not manage or control the underlying cloud
infrastructure including networks, servers, operating systems, or
storage, but has control over the deployed applications and
possibly application hosting environment configurations.
Infrastructure as a Service is the capability provided to the
consumer to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure, but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components, such as, for example, host
firewalls.
[0049] Deployment models may include, for example, a private cloud,
community cloud, public cloud, and hybrid cloud. A private cloud is
a cloud infrastructure operated solely for an organization. The
private cloud may be managed by the organization or a third party
and may exist on-premises or off-premises. A community cloud is a
cloud infrastructure shared by several organizations and supports a
specific community that has shared concerns, such as, for example,
mission, security requirements, policy, and compliance
considerations. The community cloud may be managed by the
organizations or a third party and may exist on-premises or
off-premises. A public cloud is a cloud infrastructure made
available to the general public or a large industry group and is
owned by an organization selling cloud services. A hybrid cloud is
a cloud infrastructure composed of two or more clouds, such as, for
example, private, community, and public clouds, which remain as
unique entities, but are bound together by standardized or
proprietary technology that enables data and application
portability, such as, for example, cloud bursting for
load-balancing between clouds.
[0050] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0051] With reference now to FIG. 3, a diagram illustrating a cloud
computing environment is depicted in which illustrative embodiments
may be implemented. In this illustrative example, cloud computing
environment 300 includes a set of one or more cloud computing nodes
310 with which local data processing systems used by cloud
consumers may communicate. Cloud computing nodes 310 may be, for
example, server 104 and server 106 in FIG. 1. Local data processing
systems that communicate with cloud computing nodes 310 include
data processing system 320A, which may be a personal digital
assistant or a smart phone, data processing system 320B, which may
be a desktop computer or a network computer, data processing system
320C, which may be a laptop computer, and data processing system
320N, which may be a computer system of an automobile. Data
processing systems 320A-320N may be, for example, clients 110-114
in FIG. 1.
[0052] Cloud computing nodes 310 may communicate with one another
and may be grouped physically or virtually into one or more cloud
computing networks, such as a private cloud computing network, a
community cloud computing network, a public cloud computing
network, or a hybrid cloud computing network. This allows cloud
computing environment 300 to offer infrastructure, platforms,
and/or software as services without requiring the cloud consumers
to maintain these resources on their local data processing systems,
such as data processing systems 320A-320N. It is understood that
the types of data processing devices 320A-320N are intended to be
examples only and that cloud computing nodes 310 and cloud
computing environment 300 can communicate with any type of
computerized device over any type of network and/or network
addressable connection using a web browser, for example.
[0053] With reference now to FIG. 4, a diagram illustrating an
example of abstraction layers of a cloud computing environment is
depicted in accordance with an illustrative embodiment. The set of
functional abstraction layers shown in this illustrative example
may be implemented in a cloud computing environment, such as cloud
computing environment 300 in FIG. 3. Also, it should be noted that
the layers, components, and functions shown in FIG. 4 are intended
to be examples only and not intended to be limitations on
illustrative embodiments.
[0054] In this example, abstraction layers of a cloud computing
environment 400 includes hardware and software layer 402,
virtualization layer 404, management layer 406, and workloads layer
408. Hardware and software layer 402 includes the hardware and
software components of the cloud computing environment. The
hardware components may include, for example, mainframes 410, RISC
(Reduced Instruction Set Computer) architecture-based servers 412,
servers 414, blade servers 416, storage devices 418, and networks
and networking components 420. In some illustrative embodiments,
software components may include, for example, network application
server software 422 and database software 424.
[0055] Virtualization layer 404 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 426; virtual storage 428; virtual networks 430
including virtual private networks; virtual applications and
operating systems 432; and virtual machines 434.
[0056] Management layer 406 may provide a plurality of different
management functions, such as, for example, resource provisioning
436, metering and pricing 438, security and user portal 440,
service level management 442, and virtual machine environment
management 444. Resource provisioning 436 dynamically procures
computing resources and other resources, which are utilized to
perform workloads or tasks within the cloud computing environment.
Metering and pricing 438 provides cost tracking as resources are
utilized within the cloud computing environment and billing for
consumption of these resources. In one example, these resources may
comprise application software licenses. Security of security and
user portal 440 provides identity verification for cloud consumers
and workloads, as well as protection for data and other resources.
User portal of security and user portal 440 provides access to the
cloud computing environment for cloud consumers and system
administrators. Service level management 442 provides cloud
computing resource allocation and management such that required
service levels are met based on service level agreements. Virtual
machine environment management 444 provides management of virtual
machine migration from a source virtual machine environment, such
as a data center, to a target virtual machine environment, such as
a cloud.
[0057] Workloads layer 408 provides the functionality of the cloud
computing environment. Example workloads and functions provided by
workload layer 408 may include mapping and navigation 446, software
development and lifecycle management 448, virtual classroom
education delivery 450, data analytics processing 452, transaction
processing 454, and migrating client workloads and backup data from
source to target virtual machine environments 456.
[0058] In the course of developing illustrative embodiments, it was
discovered that in order for newly migrated workloads to take full
advantage of a cloud environment, a transformation from legacy
management services corresponding to a source environment to cloud
native services corresponding to a target environment is needed.
For example, an automated backup configuration transformation from
a source backup manager type service to a target native cloud
service or architecture is needed. Typically, this workload
migration is a disruptive process and customer data may be lost. In
addition, during the workload migration data backup is discontinued
in the old source legacy environment and not yet set up in the new
target cloud environment. Further, setting up a data backup
configuration is still largely a manual task post-migration.
[0059] Illustrative embodiments migrate all workloads and their
corresponding data concurrently from a source legacy environment
into a new cloud environment or into two or more different cloud
environments. Illustrative embodiments may perform the workload
migration in waves, meaning that a number of virtual machine images
and their corresponding data are moved from one environment into
another. The determination of which virtual machine images belong
to a single wave is based on a number of characteristics. One
characteristic is data dependency. For example, illustrative
embodiments may migrate all virtual machine images that are using
the same data (e.g., having read or write access to the same data)
in the same wave.
[0060] Illustrative embodiments automatically analyze a data backup
configuration of the source environment. In one illustrative
embodiment, the illustrative embodiment performs a data backup with
a backup manager, where one or more backup manager servers are
receiving backup data. Illustrative embodiments arrange data
backups so that there are occasional full data backups followed by
incremental data backups. Illustrative embodiments allocate all
virtual machine images to one backup manager server and send data
for incremental data backup periodically. Illustrative embodiments
write data in the order the data are received, meaning that backup
data segments from different virtual machine images are saved
sequentially and intermingled on storage or tapes. In addition,
illustrative embodiments may encode data for data security. Before
workload migration, illustrative embodiments perform a full data
backup for all virtual machine images in a particular wave.
[0061] Once illustrative embodiments migrate a wave of virtual
machine images into a new target environment, such as a cloud,
illustrative embodiments establish a new backup configuration
corresponding to the new target environment. In one illustrative
embodiment, the illustrative embodiment may perform the migration
with a backup manager configuration. In another illustrative
embodiment, the illustrative embodiment may utilize some other data
backup configuration.
[0062] Illustrative embodiments identify patterns of existing
(e.g., legacy) backup configurations and propose an automated
approach for transforming the existing data backup configuration to
a new cloud-enabled data backup configuration. This approach is
based on artificial intelligence (AI) planning, which illustrative
embodiments utilize to dynamically assemble a set of services, such
as, for example, application programming interfaces, to automate
the process of data backup configuration transformation from a
source data backup configuration to a target data backup
configuration. Because multiple classes of client workloads and
their corresponding data backup configurations may exist,
illustrative embodiments utilize this dynamic/adaptation approach
in real time.
[0063] Thus, illustrative embodiments provide for automatic
conversion to the new service management stack and registration
with the appropriate services to handle the backup configuration
transformation process based on automation patterns that specify
atomic and complex actions for transformation, which enables
interleaved or concurrent workload migration and data backup
management processes. As a result, illustrative embodiments may
reduce the time for full steady state data backup configuration
transformation to cloud native services. In addition, illustrative
embodiments also may decrease ongoing platform and support
costs.
[0064] With reference now to FIG. 5, a diagram of an example of a
migration process is depicted in accordance with an illustrative
embodiment. Migration process 500 may be implemented in a network
of data processing systems, such as, for example, network data
processing system 100 in FIG. 1. In addition, migration process 500
may be performed by a backup and migration manager, such as, for
example, backup and migration manager 218 in data processing system
200 in FIG. 2.
[0065] During migration process 500, the backup and migration
manager migrates a client workload from source environment 502 to
target environment 504. Source environment 502 may be, for example,
a data center environment. Target environment 504 may be, for
example, another data center environment or a cloud
environment.
[0066] The backup and migration manager performs workload migration
506 and backup data migration 508 from source environment 502 to
target environment 504. Workload migration 506 represents the
migration of a set of one or more client workloads with all
corresponding virtual machine images. Backup data migration 508
represents the migration of all backed up data corresponding to the
set of client workloads being migrated in workload migration 506.
It should be noted that the backup and migration manager may
perform workload migration 506 and backup data migration 508
concurrently.
[0067] The backup and migration manager automatically identifies
patterns of the existing data backup configuration (i.e., source
backup configuration 510), which corresponds to source environment
502. In addition, the backup and migration manager automatically
identifies patterns of the new data backup configuration (i.e.,
target backup configuration 512), which corresponds to target
environment 504. The backup and migration manager also analyzes the
characteristics, such as data dependencies, of source backup
configuration 510 and then automatically maps those characteristics
to characteristics of target backup configuration 512 corresponding
to target environment 504. After mapping the characteristics of the
two data backup configurations, the backup and migration manager
automatically determines and generates a backup configuration
transformation solution or plan. The backup configuration
transformation solution may be, for example, backup configuration
transformation plan 228 in FIG. 2.
[0068] Further, the backup and migration manager assembles a set of
one or more services, such as application programming interfaces,
using artificial intelligence planning. The backup and migration
manager implements the backup configuration transformation plan
using the set of assembled services. The backup and migration
manager automatically performs data backup in target environment
504 while executing the backup configuration transformation
solution.
[0069] As an example use case, source environment 502 utilizes a
tape-based data backup configuration. The tapes are stored in a
vault and data recovery is from the tapes. Target environment 504
offers a remote tape-based data backup configuration. The data in
backup data migration 508 are de-duplicated and copied into another
data center. Data recovery is based on data center data.
[0070] As another example use case, source environment 502 utilizes
a tape-based data backup configuration. Target environment 504
offers a remote disk-based data backup configuration using data
mirroring, for example. Target environment 504 does not offer data
backup to tape capability. As the two example use cases illustrate
above, the backup and migration manager will have to determine a
backup configuration transformation plan for transforming source
backup configuration 510 to target backup configuration 512 to
accommodate backup data migration 508.
[0071] With reference now to FIG. 6, a diagram of an example of an
alternate migration process is depicted in accordance with an
illustrative embodiment. Alternate migration process 600 may be
implemented in a network of data processing systems, such as, for
example, network data processing system 100 in FIG. 1. In addition,
alternate migration process 600 may be performed by a backup and
migration manager, such as, for example, backup and migration
manager 218 in data processing system 200 in FIG. 2.
[0072] During alternate migration process 600, the backup and
migration manager migrates a client workload from source
environment 602 to target hybrid environment 604. Source
environment 602 may be, for example, a data center environment.
Target hybrid environment 604 may be, for example, a combination of
another data center environment and a cloud environment, a
combination of different cloud environments, or any type
combination of different data processing environments. In this
example, target hybrid environment 604 includes public cloud 606
and private cloud 608.
[0073] The backup and migration manager performs workload migration
610, backup data migration 612, and backup data migration 614 from
source environment 602 to target hybrid environment 604. Workload
migration 610 represents the migration of a set of one or more
client workloads with all corresponding virtual machine images.
Backup data migration 612 and backup data migration 614 represent
the migration of all backed up data corresponding to the set of
client workloads being migrated in workload migration 610. However,
it should be noted in this example that the backup and migration
manager sends backup data migration 612 to public cloud 606 and
sends backup data migration 614 to private cloud 608. Also, it
should be noted that the backup and migration manager may perform
workload migration 610, backup data migration 612, and backup data
migration 614 concurrently.
[0074] The backup and migration manager automatically identifies
patterns of the existing data backup configuration (i.e., source
backup configuration 616), which corresponds to source environment
602. In addition, the backup and migration manager automatically
identifies patterns of the new set of data backup configurations
(i.e., target backup configuration A 618 and target backup
configuration B 620), which correspond to public cloud 606 and
private cloud 608, respectively, in target hybrid environment 604.
The backup and migration manager also analyzes the characteristics
of source backup configuration 616 and then automatically maps
those characteristics to characteristics of target backup
configuration A 618 corresponding to public cloud 606 and
characteristics of target backup configuration B 620 corresponding
to private cloud 608. After mapping the characteristics of the
different data backup configurations, the backup and migration
manager automatically determines and generates a backup
configuration transformation plan. The backup configuration
transformation plan may be, for example, backup configuration
transformation plan 228 in FIG. 2.
[0075] Further, the backup and migration manager assembles a set of
one or more services, such as application programming interfaces,
using artificial intelligence planning. The backup and migration
manager implements the backup configuration transformation plan
using the set of assembled services. The backup and migration
manager automatically performs data backup in public cloud 606 and
private cloud 608 while executing the backup configuration
transformation plan.
[0076] With reference now to FIG. 7, a specific example of backup
configuration transformation inputs is depicted in accordance with
an illustrative embodiment. Backup configuration transformation
inputs 700 may be, for example, backup configuration transformation
inputs 226 in FIG. 2. In this example, backup configuration
transformation inputs 700 include state of source 702, goal state
of target 704, and domain description 706.
[0077] State of source 702 may be, for example, state of source
environment 242 in FIG. 2. State of source 702 defines a current
state of a source environment, such as source environment 502 in
FIG. 5. Goal state of target 704 may be, for example, goal state of
target environment 244 in FIG. 2. Goal state of target 704 defines
a goal state of a target environment, such as target environment
506 in FIG. 5, after migration of a workload and its corresponding
backup data, such as workload migration 506 and backup data
migration 508 in FIG. 5.
[0078] As an example of goal state of target 704, application X,
along with virtual servers S1, S2, S3, are to be migrated to a
target hybrid cloud environment. Specifically, virtual server S1 is
to be migrated to Cloud Cl (i.e., virtual server S1 is a processing
module in a public cloud, such as public cloud 606 in FIG. 6). In
addition, virtual servers S2 and S3 are to be migrated to Cloud C2
(i.e., virtual servers S2 and S3 store sensitive data in a private
cloud, such as private cloud 608 in FIG. 6). Application X has the
following data backup configuration: backup manager managing
virtual servers S1, S2, and S3. Process starts migration of virtual
sever S1 to public Cloud C1 and migration of virtual servers S2 and
S3 to private Cloud C2.
[0079] Domain description 706 may be, for example, backup
configuration transformation contextual actions 246 in FIG. 2.
Domain description 706 defines backup configuration transformation
actions in terms of input, output, preconditions, and
post-condition effects.
[0080] With reference now to FIGS. 8A-8C, a flowchart illustrating
a process for managing data backup during workload migration is
shown in accordance with an illustrative embodiment. The process
shown in FIGS. 8A-8C may be implemented in a computer, such as, for
example, server 104 in FIG. 1 and data processing system 200 in
FIG. 2.
[0081] The process begins when the computer identifies a set of one
or more workloads for migration from a source environment to a
target environment in response to receiving a request to migrate
the set of one or more workloads (step 802). The computer analyzes
characteristics of a backup configuration corresponding to the
source environment for each workload in the set of one or more
workloads for migration to the target environment (step 804). In
addition, the computer analyzes characteristics of a set of one or
more backup configurations corresponding to the target environment
(step 806).
[0082] Afterward, the computer performs semantic matching between
the characteristics of the backup configurations corresponding to
the source environment and the target environment for each backup
capability (step 808). The computer also defines a state of the
source environment (step 810). Further, the computer defines backup
configuration transformation contextual actions by representing
each workload migration step in a set of one or more workload
migration steps in terms of input, output, precondition, and
post-condition effect (step 812). Furthermore, the computer defines
a goal state of the target environment (step 814).
[0083] Subsequently, the computer initiates the migration of the
set of one or more workloads from the source environment to the
target environment along with migration of backup data
corresponding to the set of one or more workloads (step 816). In
addition, the computer determines backup configuration
transformation from the backup configuration corresponding to the
source environment to the set of one or more backup configurations
corresponding to the target environment based on the semantic
matching between the characteristics of the backup configurations,
the state of the source environment, the backup configuration
transformation contextual actions, and the goal state of the target
environment (step 818). Then, the computer generates a backup
configuration transformation plan based on the determined backup
configuration transformation from the backup configuration
corresponding to the source environment to the set of one or more
backup configurations corresponding to the target environment (step
820).
[0084] Further, the computer executes the backup configuration
transformation plan (step 822). The computer also monitors the
backup configuration transformation for exceptions (step 824). The
computer makes a determination as to whether a backup configuration
transformation exception exists (step 826).
[0085] If the computer determines that a backup configuration
transformation exception does not exist, no output of step 826,
then the computer completes the migration of the set of one or more
workloads from the source environment to the target environment
along with the migration of the backup data corresponding to the
set of one or more workloads based on the backup configuration
transformation plan (step 828). In addition, the computer stores
the backup configuration transformation plan and the request to
migrate the set of one or more workloads in a storage device (step
830). Thereafter, the process terminates.
[0086] Returning again to step 826, if the computer determines that
a backup configuration transformation exception does exist, yes
output of step 826, then the computer makes a determination as to
whether the backup configuration transformation exception
corresponds to an unknown backup configuration (step 832). If the
computer determines that the backup configuration transformation
exception does correspond to an unknown backup configuration, yes
output of step 832, then the process returns to step 802 where the
process starts again. If the computer determines that the backup
configuration transformation exception does not correspond to an
unknown backup configuration, no output of step 832, then the
computer makes a determination as to whether the backup
configuration transformation exception corresponds to a new data
backup technology (step 834).
[0087] If the computer determines that the backup configuration
transformation exception does correspond to a new data backup
technology, yes output of step 834, then the process returns to
step 812 where the computer defines the backup configuration
transformation contextual actions. If the computer determines that
the backup configuration transformation exception does not
correspond to a new data backup technology, no output of step 834,
then the computer makes a determination as to whether the backup
configuration transformation exception corresponds to a change in
the target environment (step 836).
[0088] If the computer determines that the backup configuration
transformation exception does correspond to a change in the target
environment, yes output of step 836, then the process returns to
step 806 where the computer analyzes characteristics of a set of
one or more backup configurations corresponding to the new target
environment. If the computer determines that the backup
configuration transformation exception does not correspond to a
change in the target environment, no output of step 836, then the
computer determines that the backup configuration transformation
exception corresponds to an unknown exception (step 838).
[0089] Subsequently, the computer sends a notification to a subject
matter expert to review the unknown exception (step 840).
Afterward, the computer receives a set of one or more modifications
to the backup configuration transformation plan based on the review
of the subject matter expert of the unknown exception (step 842).
The computer modifies the backup configuration transformation plan
based on the set of one or more modifications (step 844).
Thereafter, the process returns to step 820 where the computer
generates a new backup configuration transformation plan.
[0090] Thus, illustrative embodiments provide a
computer-implemented method, computer system, and computer program
product for managing data backup configuration transformation from
a data backup configuration corresponding to a source virtual
machine environment to a set of data backup configurations
corresponding to a target virtual machine environment during
migration of a set of workloads from the source virtual machine
environment to the target virtual machine environment.
[0091] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiment. The terminology used herein
was chosen to best explain the principles of the embodiment, the
practical application or technical improvement over technologies
found in the marketplace, or to enable others of ordinary skill in
the art to understand the embodiments disclosed here.
[0092] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
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